Instructions to use Rafeq/donate_cry_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rafeq/donate_cry_classification with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Rafeq/donate_cry_classification") model = Wav2Vec2ForSpeechClassification.from_pretrained("Rafeq/donate_cry_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a2b7eb2690da46e6256325a4c2e09d6cadf59ff26c0fdf5e171d905e98838f3
- Size of remote file:
- 1.27 GB
- SHA256:
- 6490ddd6cc898d405029c9d71e25a84e4f6cfb50ba300fb664bc65fdb4505074
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.